Active implantable medical devices are known that include an endocardial acceleration sensor (an “EA sensor”). The signal delivered by an EA sensor (hereinafter “EA signal”) is processed and analyzed by various algorithms for, among other things, diagnosis, control of the device, and search for pacing configurations providing the best hemodynamic efficiency.
Several clinical studies have shown that endocardial acceleration is a parameter that accurately and in real-time reflects the phenomena related to the contractions and relaxations of the heart muscle, and can therefore provide comprehensive information on the cardiac mechanics in the case of normal operation as well as in the case of a deficient operation.
The EA signal can be decomposed into two components corresponding to the two successive major heart sounds (S1 and S2 sounds of the phonocardiogram): the EA1 component, which begins following the QRS complex, is caused by a combination of closing the atrioventricular valves, opening the semilunar valves, and the contraction of the left ventricle; the EA2 component, which follows, is associated with the end of the ventricular systole and is generated by the closure of the semilunar valves. The EA signal may contain one or two other distinguishable components, called EA3 and EA4, respectively corresponding to the S3 and S4 sounds of the phonocardiogram.
EP 2092885 A1 and its counterpart US Patent Publication No. 2009/0209875 (both assigned to Sorin CRM S.A.S., previously known as ELA Medical) describes a device implementing an improved technique for EA signal analysis which allows extraction of some meaningful information, representative of the hemodynamic and mechanical activity of the patient's heart. This technique operates a signal averaging and a realignment of its components over several successive cycles. This effectively eliminates the influence of cycle to cycle variations of the EA signal which may distort the results delivered by the algorithm analyzing the EA signal.
Specifically, this technique performs a pre-processing of the continuously collected EA signal, which:                Divides the EA signal into sub-signals, each sub-signal corresponding to the duration of one cardiac cycle and being identified by a cycle start marker representing the separation of cycles;        Segments each of the sub-signals in order to individualize the EA1 and EA2 components in a given time window;        For the current EA1 (or EA2) component thus isolated on a given cycle, searches for a cross-correlation peak with respect to the EA1 (or EA2) components collected from other cycles;        Calculates a corresponding time calibration;        Applies the calculated time calibration to the current component, so as to align it with respect to the other components, and        Averages the various sub-signals as realigned in order to produce an average EA signal for a cycle, with elimination of the bias of the cycle to cycle variability.        
The EA signal averaging over several cycles reduces the influence of cycle to cycle variations of the signal, but it introduces a time constant that becomes larger as the average is calculated over a larger number of cycles.
The choice of the preprocessing parameters of the EA signal is therefore based on a compromise between accuracy (which increases as the averaging is performed over a larger number of cycles) and fast response.
Thus, a calculation based on a relatively small number of cycles, typically five cycles as described in the aforementioned document, provides a near real-time monitoring of the evolution of the EA signal, but at the cost of some noise contamination due to the instability of the EA signal, and thus less reflects the mechanical and hemodynamic activity of the patient's heart. Similarly, the criteria for acceptance or rejection of data for a given cycle in the average calculation is more or less rigorous depending on whether the reference cycle is calculated based on a large number of elementary cardiac cycles, with consequently a higher or lower risk of introducing atypical cycles (such as extrasystoles) in the computation of the average.